基于自适应遗传聚类算法的物流越库配送仿真  

Simulation of Cross Warehouse Logistics Distribution Based on Adaptive Genetic Clustering Algorithm

作　　者：李萍萍 LI Pingping(Xuancheng Vocational And Technical College,College of Education and management,Xuancheng 242000,China)

机构地区：[1]宣城职业技术学院教育与管理学院,安徽宣城242000

出　　处：《安阳工学院学报》2022年第6期79-82,共4页Journal of Anyang Institute of Technology

基　　金：2021年度安徽高校人文社会科学研究项目“农业产业结构调整对农民收入的影响—以宣城市为例”(SK2021A0991);宣城职业技术学院科研振兴计划“宣城市农业产业结构调整对农民收入的影响”(ZXTS201901)。

摘　　要：物流配送路径的合理性较差会导致实际配送的成本和效率难以得到有效控制,为此,开展了基于自适应遗传聚类算法的物流越库配送仿真研究。结合物流公司和用户对物流配送的要求,将配送路程、配送时间以及配送成本问题统一为配送路径总成本问题,将基础运输成本和运输拥堵成本最小化作为规划目标。利用采用了自适应遗传聚类算法的路径进行规划阶段,将运输时间和运输成本平衡关系作为适应度函数,根据车辆的货载上限,对处于同一不重复路径上的目标配送点进行聚类处理,直至聚类覆盖所有节点。测试结果表明,对应配送路径的时间开销和成本开销均明显降低,配送过程中的重复行程也稳定在11.50%以内。Due to the low rationality of the logistics distribution path, it is difficult to effectively control the cost and efficiency of the actual distribution. Therefore, a simulation study of logistics cross warehouse distribution based on adaptive genetic clustering algorithm is proposed. According to the requirements of logistics companies and users for logistics distribution, the distribution distance, distribution time and distribution cost are unified as the total cost of distribution route, and the basic transportation cost and transportation congestion cost are minimized as the planning objectives. In the stage of planning the specific route by using the adaptive genetic clustering algorithm, the balance relationship between the transportation time and the transportation cost is taken as the fitness function. According to the upper limit of the vehicle’s cargo load, the target distribution points on the same non repeated route are clustered until the clustering covers all nodes. The test results show that the time and cost of the corresponding distribution route are significantly reduced, and the repeated trips in the distribution process are also stable within 11.50%.

关 键 词：自适应遗传聚类算法 物流越库配送 配送路径总成本 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]